Lead Generation Expert Knowledge

Ideal Customer Profile (ICP) Construction

A good ICP answers these questions:

1. Industry: What vertical does your ideal customer operate in?
2. Company size: How many employees? What revenue range?
3. Geography: Where are they located?
4. Technology: What tech stack do they use?
5. Budget signals: Are they funded? Growing? Hiring?
6. Decision-maker: Who has buying authority? (title, seniority)
7. Pain points: What problems does your product solve for them?

Company Size Categories

Web Research Techniques for Lead Discovery

Search Query Patterns

# Find companies in a vertical
"[industry] companies" site:crunchbase.com
"top [industry] startups [year]"
"[industry] companies [city/region]"

# Find decision-makers
"[title]" "[company]" site:linkedin.com
"[company] team" OR "[company] about us" OR "[company] leadership"

# Growth signals (high-intent leads)
"[company] hiring [role]" — indicates budget and growth
"[company] series [A/B/C]" — recently funded
"[company] expansion" OR "[company] new office"
"[company] product launch [year]"

# Technology signals
"[company] uses [technology]" OR "[company] built with [technology]"
site:stackshare.io "[company]"
site:builtwith.com "[company]"

Source Quality Ranking

1. Company website (About/Team pages) — most reliable for personnel
2. Crunchbase — funding, company details, leadership
3. LinkedIn (public profiles) — titles, tenure, connections
4. Press releases — announcements, partnerships, funding
5. Job boards — hiring signals, tech stack requirements
6. Industry directories — comprehensive company lists
7. News articles — recent activity, reputation
8. Social media — engagement, company culture

Lead Enrichment Patterns

Basic Enrichment (always available)

• Full name (first + last)
• Job title
• Company name
• Company website URL

Standard Enrichment

• Company employee count (from About page, Crunchbase, or LinkedIn)
• Company industry classification
• Company founding year
• Technology stack (from job postings, StackShare, BuiltWith)
• Social profiles (LinkedIn URL, Twitter handle)
• Company description (from meta tags or About page)

Deep Enrichment

• Recent funding rounds (amount, investors, date)
• Recent news mentions (last 90 days)
• Key competitors
• Estimated revenue range
• Recent job postings (growth signals)
• Company blog/content activity (engagement level)
• Executive team changes

Email Pattern Discovery

Common corporate email formats (try in order):

1. [email protected] (most common for small companies)
2. [email protected] (most common for larger companies)
3. [email protected] (e.g., jsmith@)
4. [email protected] (e.g., johns@)

Note: NEVER send unsolicited emails. Email patterns are for reference only.

Lead Scoring Framework

Scoring Rubric (0-100)

ICP Match (30 points max):
  Industry match:     +10
  Company size match: +5
  Geography match:    +5
  Role/title match:   +10

Growth Signals (20 points max):
  Recent funding:     +8
  Actively hiring:    +6
  Product launch:     +3
  Press coverage:     +3

Enrichment Quality (20 points max):
  Email found:        +5
  LinkedIn found:     +5
  Full company data:  +5
  Tech stack known:   +5

Recency (15 points max):
  Active this month:  +15
  Active this quarter:+10
  Active this year:   +5
  No recent activity: +0

Accessibility (15 points max):
  Direct contact:     +15
  Company contact:    +10
  Social only:        +5
  No contact info:    +0

Score Interpretation

Deduplication Strategies

Matching Algorithm

1. Exact match: Normalize company name (lowercase, strip Inc/LLC/Ltd) + person name
2. Fuzzy match: Levenshtein distance < 2 on company name + same person
3. Domain match: Same company website domain = same company
4. Cross-source merge: Same person at same company from different sources → merge enrichment data

Normalization Rules

Company name:
  - Strip legal suffixes: Inc, LLC, Ltd, Corp, Co, GmbH, AG, SA
  - Lowercase
  - Remove "The" prefix
  - Collapse whitespace

Person name:
  - Lowercase
  - Remove middle names/initials
  - Handle "Bob" = "Robert", "Mike" = "Michael" (common nicknames)

Output Format Templates

CSV Format

Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes
"Jane Smith","VP Engineering","Acme Corp","https://acme.com","https://linkedin.com/in/janesmith","SaaS","SMB (120 employees)",85,"2025-01-15","Series B funded, hiring 5 engineers"

JSON Format

[
  {
    "name": "Jane Smith",
    "title": "VP Engineering",
    "company": "Acme Corp",
    "company_url": "https://acme.com",
    "linkedin": "https://linkedin.com/in/janesmith",
    "industry": "SaaS",
    "company_size": "SMB",
    "employee_count": 120,
    "score": 85,
    "discovered": "2025-01-15",
    "enrichment": {
      "funding": "Series B, $15M",
      "hiring": true,
      "tech_stack": ["React", "Python", "AWS"],
      "recent_news": "Launched enterprise plan Q4 2024"
    },
    "notes": "Strong ICP match, actively growing"
  }
]

Markdown Table Format

| # | Name | Title | Company | Score | Key Signal |
|---|------|-------|---------|-------|------------|
| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | Series B funded, hiring |
| 2 | John Doe | CTO | Beta Inc | 72 | Product launch Q1 2025 |

Compliance & Ethics

DO

• Use only publicly available information
• Respect robots.txt and rate limits
• Include data provenance (where each piece of info came from)
• Allow users to export and delete their lead data
• Clearly mark confidence levels on enriched data

DO NOT

• Scrape behind login walls or paywalls
• Fabricate any lead data (even "likely" email addresses without evidence)
• Store sensitive personal data (SSN, financial info, health data)
• Send unsolicited communications on behalf of the user
• Bypass anti-scraping measures (CAPTCHAs, rate limits)
• Collect data on individuals who have opted out of data collection

Data Retention

• Keep lead data in local files only — never exfiltrate
• Mark stale leads (>90 days without activity) for review
• Provide clear data export in all supported formats